code: def train_network(
      env,
      network,
      memory,
      n_games=200,
      max_score=200,
      epsilon=0.1,
      gamma=0.9,
  ):
      for episode in range(1, n_games + 1):
          state = env.reset()
  
          for t in range(max_score):
              if random.random() <= epsilon:
                  # Select random action with probability
                  # equal to the `epsilon`
                  action = random.randint(0, 1)
              else:
                  # Use action selected by the network
                  q = network.predict(state)
                  action = int(np.argmax(q[0]))
  
              new_state, reward, done, info = env.step(action)
              memory.append((state, action, reward, done, new_state))
  
              if done:
                  # We done when network lost the game.
                  # Low reward will penalyze network.
                  reward = -10
  
              if len(memory) == memory_size:
                  # Train only when we collected enough samples
                  x_train, y_train = training_samples(
                      network, memory, gamma
                  )
                  network.train(x_train, y_train, epochs=1)
                  loss = network.errors.train[-1]
  
              state = new_state
  
              if done:
                  break
  
          if len(memory) == memory_size:
              print(
                  "Game #{:<3} | Lost after {:<3} iterations | loss: {:.4}"
                  "".format(episode, t + 1, loss)
              )
          else:
              print(
                  "Game #{:<3} | Lost after {:<3} iterations"
                  "".format(episode, t + 1)
              )
  
file_path: neupy/examples/reinforcement_learning/rl_cartpole.py
imports: import os
  import random
  import argparse
  from collections import deque
  import gym
  import numpy
  from neupy import layers, algorithms, utils, storage
label_desc: Use image node_id 3 for calling a global function with example usage: train_network(env, network, memory, n_games, max_score, epsilon, gamma) without return types